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Analytics in Arts and Culture

Учебный год
Обучение ведется на английском языке
Курс обязательный
Когда читается:
1-й курс, 3, 4 модуль


Course Syllabus


Art analytics allows the industry to measure the financial and intrinsic value of each art piece with greater accuracy. Exceptionally popular art pieces can be auctioned for millions of dollars because there is no other duplicate piece available. However, the price of a single art piece is often decided without important context like the artist’s other completed works. Art analytics promises to address this situation by pulling information from different sources to give a more well-rounded view of the art piece’s value. In addition to the economic value of art, there are also social and educational benefits as well. Art brings cultural value to any society, but measuring that value in precise numbers has always been a challenge. However, due to art analytics, it is becoming easier to measure the intrinsic value art brings, not just to our homes but the public as well. Art analytics makes use of new data like social media and user-generated sites that make it easier to measure the emotional and mental effects of art. Art and the wider cultural sector can diversify their business models and discover other avenues for revenue thanks to art analytics. Art institutions can experiment with different business models without risking their hard-earned capital. Art analytics uses sophisticated algorithms to analyse data to make predictions on how targeted customers respond to new art events. For example, will customers pay for live-stream theatre? With analytics, institutions will be emboldened to try out new forms of art and develop new experiences that will expand their audience.
Learning Objectives

Learning Objectives

  • Develop students' holistic understanding of the methodology of scientific and analytical research
  • Develop students’ skills in the use of research tools, both for planning, preparing and conducting research projects in the framework of writing a term paper and master's thesis, and for performing and evaluating research and analytical work
  • Master students' capabilities to develop and implement various research strategies
Expected Learning Outcomes

Expected Learning Outcomes

  • Able to define the basic principles and peculiarities of the research and analytical method
  • Know the main methods of qualitative and quantitative research
  • Understand the difference between the aims of qualitative and quantitative research
  • to be able to find the appropriate data
  • to be able to collect qualitative or quantitative data
  • to understand the sampling issues as well as bias and validity issues
  • to be able to identify the type of data and type of variable
  • to be able to prepare the data for the analysis
  • to be able to use structured or unstructured data for the analysis
  • to be able to measure the center and the location of the variable
  • to be able to measure the variation of the variable
  • to use the appropriate type of visualization
  • to be able to formulate the hypothesis
  • to be able to test the hypothesis
  • to be able to use factor analysis and principal component analysis
  • to be able to analyse data using quantitative or qualitative methods
  • to be able to use regression analysis
  • to interpret the results of analysis
Course Contents

Course Contents

  • Introduction to analytics: research methods
  • Data sources and data collection
  • Data types and data preparation
  • Data description and visualization
  • Hypothesis testing
  • Data analysis
Assessment Elements

Assessment Elements

  • non-blocking Tests
    Electronic tests in LMS for 15-30 minutes
  • non-blocking Homeworks
  • non-blocking Project
    Project defense (15 slides presentation). Written report must be submitted beforehand. Students carry out a project to apply the models and methods studied in the course to address the question from arts and culture field. Students independently choose a subject area, form a problem statement, select the necessary data, perform analysis and interpret results.
  • non-blocking Final test
Interim Assessment

Interim Assessment

  • 2023/2024 4th module
    0.3 * Final test + 0.15 * Homeworks + 0.1 * Homeworks + 0.2 * Project + 0.1 * Tests + 0.15 * Tests


Recommended Core Bibliography

  • Fabbris, L., & Davino, C. (2013). Survey Data Collection and Integration. Springer.
  • Groebner, David, et al. Business Statistics, EBook, Global Edition, Pearson Education, Limited, 2018. ProQuest Ebook Central, https://ebookcentral.proquest.com/lib/hselibrary-ebooks/detail.action?docID=5186156.
  • HU, C.-P., & CHANG, Y.-Y. (2017). John W. Creswell, Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.BCEBF1CE
  • Nabavi, M., & Olson, D. L. (2019). Introduction to Business Analytics. New York: Business Expert Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1922612
  • S. Christian Albright, & Wayne L. Winston. (2019). Business Analytics: Data Analysis & Decision Making, Edition 7. Cengage Learning.
  • Stock, J. H., & Watson, M. W. (2015). Introduction to Econometrics, Update, Global Edition (Vol. Updated third edition). Boston: Pearson Education. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1419285

Recommended Additional Bibliography

  • Munzert, S. (2014). Automated Data Collection with R : A Practical Guide to Web Scraping and Text Mining. HobokenChichester, West Sussex, United Kingdom: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=878670
  • Robert I. Kabacoff. (2015). R in Action : Data Analysis and Graphics with R: Vol. Second edition. Manning.